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The rapid growth of wireless communication has led to an increased demand for radio spectrum, resulting in inefficient spectrum utilization and congestion in conventional networks. Cognitive Radio Networks (CRNs) offer a promising solution by enabling dynamic and intelligent spectrum access through spectrum sensing, learning, and adaptation. This paper focuses on intelligent spectrum allocation in CRNs using machine learning and optimization techniques to enhance spectral efficiency and reduce interference. The proposed system enables secondary users to opportunistically utilize underused frequency bands without disrupting primary users. Algorithms such as reinforcement learning and fuzzy logic are applied to predict spectrum availability and allocate channels dynamically based on real-time network conditions. Simulation results show that intelligent allocation mechanisms improve throughput, minimize latency, and ensure fair spectrum sharing among users. This study contributes to developing adaptive, self-learning communication systems capable of addressing spectrum scarcity in next-generation wireless networks.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Pudi Ganesh, ganesh.pudi@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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